Clinical Studies, Big Data, and Artificial Intelligence in Nephrology and Transplantation
A special issue of Journal of Clinical Medicine (ISSN 2077-0383). This special issue belongs to the section "Nephrology & Urology".
Deadline for manuscript submissions: closed (31 December 2020) | Viewed by 94061
Special Issue Editors
Interests: artificial Intelligence; machine learning; meta-analysis; acute kidney injury; clinical nephrology; kidney transplantation
Special Issues, Collections and Topics in MDPI journals
Interests: artificial intelligence; machine Learning; nephrology; acute kidney injury; clinical nephrology; kidney transplantation
Special Issues, Collections and Topics in MDPI journals
Interests: machine learning; kidney transplantation; observational studies; statistical analysis; epidemiology
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
In recent years, artificial intelligence has increasingly been playing an essential role in diverse areas in medicine, helping clinicians in patient management. In nephrology and transplantation, artificial intelligence can be utilized to enhance clinical care, such as hemodialysis prescriptions and the follow-up of kidney transplant patients. Furthermore, there are rapidly expanding applications and validations of comprehensive, computerized medical records and related databases, including national registries, health insurance, and drug prescriptions.
In this Special Issue, we are making a call to action to stimulate researchers and clinicians to submit their invaluable works including original clinical research (single- or multi-center), database studies from registries, meta-analyses, and artificial intelligence research in nephrology including acute kidney injury, electrolytes and acid–base, chronic kidney disease, glomerular disease, dialysis, and transplantation that will provide additional knowledge and skills in the field of nephrology and transplantation to improve patient outcomes.
Potential topics include, but are not limited to, the following:
-Artificial intelligence and machine learning for predicting acute kidney injury;
-Machine learning predicting acute kidney injury and renal replacement therapy in intensive care units;
-Re-transplants compared to primary kidney transplants recipients: the OPTN/UNOS database;
-Alcohol use and development of chronic kidney disease: a nationwide database analysis;
-Association of race and poverty with mortality on maintenance dialysis using the United States Renal Data System database;
-Prevention of contrast-induced acute kidney injury in patients undergoing cardiovascular procedures—a meta-analysis;
-Systematic review and meta-analysis of renal replacement therapy modalities for acute kidney injury.
Dr. Wisit Cheungpasitporn
Dr. Charat Thongprayoon
Dr. Wisit Kaewput
Guest Editors
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Keywords
- artificial intelligence
- machine learning
- systematic review
- meta-analysis
- nephrology
- transplantation
- kidney transplantation
- electrolytes
- acute kidney injury
- chronic kidney disease
- glomerulonephritis
- end-stage kidney disease
- dialysis
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